Abstract
In cognitive radio (CR) system, secondary user (SU) should use available channels opportunistically when the primary user (PU) does not exist. In CR network, SUs have to detect the PU signal with sufficient sensing time to guarantee the detection probability and minimize the interference to the PU, while the CR system should have enough data transmission time to maximize the transmission opportunity of the SU. Therefore, the sensing time and data transmission time of the SU are generally considered as main optimization parameters to maximize the throughput of the CR system. In this paper, a separate sensing node is designated and the sensing is continuously performed using the interference alignment (IA) technique. In this paper, the designated sensing node estimates the interference ratio and transmission opportunity loss ratio. To satisfy the primary user’s interference requirement and maximize secondary throughput, we proposed dynamic adjustment mechanism for sensing slot time and sensing report interval using reinforcement learning in time-varying communication environment. The experimental results show that the proposed approach can minimize the interference on PU and enhance the transmission opportunity of SUs.
Highlights
As the demand for multimedia services explosively increases, the need for bandwidth to meet the requirements of communication systems is rapidly increasing
Cognitive radio (CR) should be able to intelligently monitor and adapt to the surrounding environment to share the frequency band with the licensed primary user (PU) in a frequency band not occupied by the PU [3]
Even if sensing is continuously performed through the multi-input multi-output (MIMO)-based CR system, still secondary systems need the optimization of the sensing and transmission time, and this optimization is very difficult to derive as a closed form specially in dynamic wireless environments in terms of time-varying channel characteristics and primary activities
Summary
As the demand for multimedia services explosively increases, the need for bandwidth to meet the requirements of communication systems is rapidly increasing. Even if sensing is continuously performed through the MIMO-based CR system, still secondary systems need the optimization of the sensing and transmission time, and this optimization is very difficult to derive as a closed form specially in dynamic wireless environments in terms of time-varying channel characteristics and primary activities. If it is too long, interference time to the PU increases due to the transmission time increases for the SUs; if it is too short, transmission performance of the SUs decreases In this regard, this paper proposes an algorithm that dynamically determines the sensing time and reporting interval of sensing result by using Q-learning based on reinforcement learning for interference control in target-level and enhancement of SU’s transmission opportunities.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: EURASIP Journal on Wireless Communications and Networking
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
